A multi-source vehicle model library automatic fusion updating method, system and program product

Abstract

The application belongs to the technical field of artificial intelligence, and specifically discloses a multi-source vehicle model library automatic fusion updating method, system and program product, which introduces similarity calculation auxiliary auditing, reduces the dependence of manual input, improves the efficiency of data updating, and reduces the error rate caused by manual operation; in combination with the intelligent strategy auxiliary auditing mode, the auditing process is simplified, the accuracy and integrity of the vehicle model library data are ensured, and the auditing time is reduced; through standardization processing of vehicle model data from different third-party sources, the problem of inconsistent data format and naming is solved, so that the information in the online vehicle model library is more unified and consistent; through the automatic updating mechanism, new vehicle model information can be integrated into the existing vehicle model library in time, so that the information obtained by the user always reflects the latest market dynamics, and decision-making errors caused by outdated information are avoided; errors or repeated items in the data can be found and corrected in time, the data quality is improved, and the reliability of the data is enhanced.

Description

Technical Field

[0001] This invention belongs to the field of artificial intelligence technology, specifically relating to a method, system, and program product for automatic fusion and updating of multi-source vehicle model databases. Background Technology

[0002] A vehicle model database is a database containing detailed information on various car brands, manufacturers, and models. It provides data support for automotive data analysis, market research, and consumer decision-making. Currently, some technical solutions have emerged for the construction and maintenance of vehicle model databases, but most still have the following shortcomings:

[0003] 1. Manual data entry and updates: Many vehicle model databases rely on manual data entry, especially when new models are launched. This method is not only inefficient, but also prone to human error, leading to inconsistencies and inaccuracies in the data.

[0004] 2. Lack of data standardization tools: Existing technologies often lack effective data standardization methods, resulting in inconsistent formats and naming conventions for data from different sources. For example, different manufacturers may use different names or classifications for the same car model, causing confusion.

[0005] 3. Limitations of centralized data management platforms: Some existing vehicle model databases adopt a centralized management model, relying on a single data source. This approach cannot effectively handle data conflicts and integration issues when multiple data sources are accessed, limiting the flexibility and scalability of the vehicle model database.

[0006] 4. Lack of timely data update mechanism: With the frequent launch of new car models on the market, the latest information cannot be reflected in a timely manner, which may cause users to encounter outdated information when searching for and using data.

[0007] 5. Inadequacy of data quality monitoring: Existing technologies often lack effective data quality monitoring mechanisms, resulting in the failure to promptly detect and correct data errors or duplicates during the data integration process.

[0008] 6. Integration and interface issues: During the integration of data from multiple sources, poor data exchange between different systems affects the integrity and availability of the vehicle model database.

[0009] 7. Insufficient Application of Artificial Intelligence and Automation: The current vehicle model database system has not fully utilized artificial intelligence technology to achieve automated data processing and updates, thus reducing the system's level of intelligence. Summary of the Invention

[0010] The purpose of this invention is to provide a method, system, and program product for automatic fusion and updating of multi-source vehicle model databases, in order to solve the above-mentioned problems existing in the prior art.

[0011] To achieve the above objectives, the present invention adopts the following technical solution:

[0012] Firstly, a method for automatic fusion and updating of multi-source vehicle model databases is provided, including:

[0013] Full data is retrieved from the third-party vehicle model database to the third-party data cache table, and full data is retrieved from the online vehicle model database to the online data cache table. The third-party vehicle model database data includes the third-party vehicle model data structure chain of the third-party vehicle model database, and the online vehicle model database data includes the online vehicle model data structure chain of the online vehicle model database.

[0014] The online vehicle data structure chain in the online data cache table is used to perform structured verification on the third-party vehicle data structure chain in the third-party data cache table to determine whether there are any structural changes in the third-party vehicle data structure chain in the third-party data cache table.

[0015] When it is determined that there are no structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table, a relationship mapping and similarity determination are performed on the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table.

[0016] Based on the relationship mapping and similarity judgment results, the corresponding online vehicle data structure chain in the online data cache table is reviewed and updated layer by layer to obtain the reviewed and updated online data cache table;

[0017] The online vehicle data in the online vehicle database is updated using the data structure chain of each online vehicle model in the updated online data cache table.

[0018] In one possible design, both the third-party vehicle data structure chain and the online vehicle data structure chain contain three levels of data: brand, manufacturer, and vehicle model, with the structure hierarchy from high to low being brand-manufacturer-vehicle model.

[0019] In one possible design, the step of using the online vehicle model data structure chain in the online data cache table to perform structured verification on the third-party vehicle model data structure chain in the third-party data cache table, and determining whether there are any structural changes in the third-party vehicle model data structure chain in the third-party data cache table, includes:

[0020] A structural mapping is performed on the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table. When there is a situation where the low-level data of the third-party vehicle model data structure chain and the corresponding online vehicle model data structure chain are the same but the high-level data are different, it is determined that there is a structural change in the corresponding third-party vehicle model data structure chain in the third-party data cache table; otherwise, it is determined that there is no structural change in the data structure chains of each third-party vehicle model in the third-party data cache table.

[0021] In one possible design, the method further includes: when it is determined that there is a structural change in the data structure chain of each third-party vehicle model in the third-party data cache table, ending the update and issuing a warning message.

[0022] In one possible design, the process of mapping relationships and determining similarity between the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table includes:

[0023] Clean the names of the data at each level of the third-party vehicle model data structure chain in the third-party data cache table, and determine the ID primary key of the data at each level of the third-party vehicle model data structure chain;

[0024] Perform relationship mapping on the online vehicle data structure chains in the online data cache table and the third-party vehicle data structure chains in the cleaned third-party data cache table to determine the third-party vehicle data structure chains and online vehicle data structure chains that have mapping relationships.

[0025] The longest common subsequence method is used to calculate the similarity between the third-party vehicle data structure chain and the online vehicle data structure chain, which have no mapping relationship, to determine the similarity of each level of data between the corresponding third-party vehicle data structure chain and the online vehicle data structure chain.

[0026] In one possible design, the step of updating the corresponding online vehicle model data structure chain in the online data cache table through hierarchical review based on relationship mapping and similarity determination results includes:

[0027] For third-party vehicle data structure chains and online vehicle data structure chains that have a mapping relationship, the ID primary keys of each level of data in the third-party vehicle data structure chain are used to rename the data at each level of the online vehicle data structure chain.

[0028] The third-party vehicle data structure chain and the online vehicle data structure chain, which do not have a mapping relationship, are reviewed and compared layer by layer. If the similarity between the two in the first M levels of data reaches the set similarity condition, the ID primary key of the first M levels of data in the third-party vehicle data structure chain is used to rename the first M levels of data in the online vehicle data structure chain accordingly. M is a positive integer in the interval [1, 3].

[0029] In one possible design, the method further includes:

[0030] When a third-party vehicle data structure chain does not have an online vehicle data structure chain with which it has a mapping relationship, and the similarity between the third-party vehicle data structure chain and the data at each level of the online vehicle data structure chain does not meet the set similarity conditions, the third-party vehicle data structure chain is directly added to the online data cache table as a new online vehicle data structure chain.

[0031] Secondly, a multi-source vehicle model database automatic fusion and update system is provided, including a data retrieval unit, a structure verification unit, a mapping matching unit, a layer-by-layer review unit, and a data update unit, wherein:

[0032] The data retrieval unit is used to retrieve all third-party vehicle model database data to a third-party data cache table and to retrieve all online vehicle model database data to an online data cache table. The third-party vehicle model database data includes the third-party vehicle model data structure chain of the third-party vehicle model database, and the online vehicle model database data includes the online vehicle model data structure chain of the online vehicle model database.

[0033] The structure verification unit is used to perform structure verification on the data structure chains of each online vehicle model in the online data cache table and the data structure chains of each third-party vehicle model in the third-party data cache table to determine whether there are any structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table.

[0034] The mapping and matching unit is used to perform relationship mapping and similarity determination on the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table when it is determined that there is no structural change in the data structure chains of each third-party vehicle model in the third-party data cache table.

[0035] The layer-by-layer review unit is used to review and update the corresponding online vehicle data structure chain in the online data cache table layer by layer based on the relationship mapping and similarity judgment results, so as to obtain the reviewed and updated online data cache table.

[0036] The data update unit is used to update the data of each online vehicle model in the online vehicle model database by using the data structure chain of each online vehicle model in the online data cache table after the review and update.

[0037] Thirdly, a multi-source vehicle model database automatic fusion and update system is provided, including:

[0038] Memory, used to store instructions;

[0039] The processor is configured to read instructions stored in the memory and execute any one of the multi-source vehicle model database automatic fusion and update methods described in the first aspect above, according to the instructions.

[0040] Fourthly, a computer-readable storage medium is provided, on which instructions are stored, which, when executed on a computer, cause the computer to perform any one of the multi-source vehicle model database automatic fusion and update methods described in the first aspect. Simultaneously, a computer program product is also provided, which, when executed on a computer, performs any one of the multi-source vehicle model database automatic fusion and update methods described in the first aspect.

[0041] Beneficial effects:

[0042] 1. Improve data update efficiency: By introducing similarity calculation to assist in the review process, the reliance on manual input is reduced, the efficiency of data updates is improved, and the error rate caused by manual operation is reduced.

[0043] 2. Achieve data standardization: By standardizing vehicle model data from different third-party sources, the problem of inconsistent data formats and naming is solved, making the information in the online vehicle model database more unified.

[0044] 3. Enhance real-time data update capabilities: Through an automatic update mechanism, new model information can be integrated into the existing model database in a timely manner, ensuring that the information obtained by users always reflects the latest market dynamics and avoiding decision-making errors caused by outdated information.

[0045] 4. Optimize the data review process: By combining intelligent strategies to assist in the review, the review process has been simplified, ensuring the accuracy and completeness of vehicle model database data and reducing review time.

[0046] 5. Enhance data quality monitoring capabilities: It can promptly detect and correct errors or duplicates in the data, thereby improving data quality and enhancing data reliability.

[0047] 6. Solve compatibility issues in data integration: By standardizing data processing procedures, the flexibility and scalability of the system can be improved, and data interoperability between different systems can be promoted.

[0048] 7. Introduce intelligent technologies: Make full use of artificial intelligence technology to realize intelligent and automated data processing, improve the overall intelligence level of the system, and thus better adapt to rapidly changing market demands. Attached Figure Description

[0049] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0050] Figure 1 This is a flowchart illustrating the method in Embodiment 1 of the present invention;

[0051] Figure 2 This is a schematic diagram of the system configuration in Embodiment 2 of the present invention;

[0052] Figure 3 This is a schematic diagram of the system configuration in Embodiment 3 of the present invention. Detailed Implementation

[0053] It should be noted that the descriptions of these embodiments are intended to aid in understanding the invention and do not constitute a limitation thereof. The specific structural and functional details disclosed herein are merely for describing exemplary embodiments of the invention. However, the invention may be embodied in many alternative forms and should not be construed as being limited to the embodiments described herein.

[0054] It should be understood that, unless otherwise explicitly specified and limited, the corresponding terms should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in the embodiments according to the specific circumstances.

[0055] Specific details are provided in the following description to provide a complete understanding of the exemplary embodiments. However, those skilled in the art will understand that the exemplary embodiments can be implemented without these specific details. For example, apparatus may be shown in block diagrams to avoid obscuring the examples with unnecessary details. In other embodiments, well-known processes, structures, and techniques may be omitted with non-essential details to avoid obscuring the embodiments.

[0056] Example 1:

[0057] This embodiment provides a method for automatic fusion and updating of multi-source vehicle model databases, which can be applied to corresponding servers, such as... Figure 1 As shown, the method includes the following steps:

[0058] S1. Fully retrieve third-party vehicle model database data to a third-party data cache table, and fully retrieve online vehicle model database data to an online data cache table. The third-party vehicle model database data includes the third-party vehicle model data structure chain of the third-party vehicle model database, and the online vehicle model database data includes the online vehicle model data structure chain of the online vehicle model database.

[0059] In practice, firstly, all data from the third-party vehicle database is retrieved to a third-party data cache table, and then all data from the online vehicle database is retrieved to an online data cache table. Subsequently, logical comparison and analysis are performed on the contents of the third-party and online data cache tables. The third-party vehicle database contains a data structure chain of third-party vehicle data, and the online vehicle database contains a data structure chain of online vehicle data. Both the third-party and online vehicle data structure chains contain three levels of data: brand, manufacturer, and vehicle model, with the structure hierarchy from highest to lowest being brand-manufacturer-vehicle model.

[0060] S2. Use the online vehicle model data structure chain in the online data cache table to perform structured verification on the third-party vehicle model data structure chain in the third-party data cache table to determine whether there are any structural changes in the third-party vehicle model data structure chain in the third-party data cache table.

[0061] In practice, a structural mapping can be performed between the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table. When there are cases where the lower-level data of a third-party vehicle model data structure chain is the same as the corresponding online vehicle model data structure chain, but the higher-level data differs (e.g., the vehicle model level data is consistent but the corresponding brand level data is inconsistent, or the vehicle model level data is consistent but the corresponding manufacturer level data is inconsistent), it is determined that the corresponding third-party vehicle model data structure chain in the third-party data cache table has undergone a structural change. Otherwise, it is determined that there is no structural change in the third-party vehicle model data structure chains in the third-party data cache table. When a structural change is determined to exist in the third-party vehicle model data structure chains in the third-party data cache table, the update must be terminated and a warning message issued.

[0062] S3. When it is determined that there are no structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table, perform relationship mapping and similarity determination on the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table.

[0063] In practice, when it is determined that there are no structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table, the names of the data at each level of the data structure chains in the third-party data cache table can be cleaned (including but not limited to removing redundant information, extracting information such as vehicle manufacturer attributes, energy data, and whether it is imported), and the ID primary key of the data at each level of the data structure chains of each third-party vehicle model can be determined. Then, a relationship mapping is performed on the data structure chains of each online vehicle model in the online data cache table and the data structure chains of each third-party vehicle model in the cleaned third-party data cache table to determine the third-party vehicle model data structure chains and online vehicle model data structure chains with mapping relationships. Then, the longest common subsequence method is used to calculate the similarity between the third-party vehicle model data structure chains and online vehicle model data structure chains that do not have mapping relationships, and to determine the similarity of the data at each level of the corresponding third-party vehicle model data structure chain and online vehicle model data structure chain.

[0064] S4. Based on the relationship mapping and similarity judgment results, the corresponding online vehicle data structure chain in the online data cache table is reviewed and updated layer by layer to obtain the reviewed and updated online data cache table.

[0065] In practice, for third-party vehicle data structure chains and online vehicle data structure chains with mapping relationships, the ID primary keys of each level of data in the third-party vehicle data structure chain are used to rename the data at each level of the online vehicle data structure chain. For third-party vehicle data structure chains and online vehicle data structure chains without mapping relationships, a layer-by-layer review and comparison is performed, that is, first the brand level is reviewed and compared, then the manufacturer level is reviewed and compared, and finally the vehicle level is reviewed and compared. If the similarity between the two in the first M levels of data reaches the set similarity condition, then the ID primary keys of the first M levels of data in the third-party vehicle data structure chain are used to rename the corresponding level of the first M levels of data in the online vehicle data structure chain, where M is a positive integer in the interval [1, 3]. When a third-party vehicle model data structure chain has no mapping relationship with an online vehicle model data structure chain, and its similarity to the data at each level of the online vehicle model data structure chains does not meet the set similarity criteria, the third-party vehicle model data structure chain is directly added to the online data cache table as a new online vehicle model data structure chain. During the data addition process, lower-level data are added directly after upper-level data is added; for example, if a brand is added, the lower-level manufacturers are added directly. The final result is the reviewed and updated online data cache table.

[0066] S5. Update the data of each online vehicle model in the online vehicle model database using the data structure chain of each online vehicle model in the online data cache table after the audit and update.

[0067] In practice, after all reviews are completed and the updated online data cache table is obtained, the online vehicle model database data can be updated directly via a backend script. This involves using the data structure chains of each online vehicle model in the updated online data cache table to update the data in each online vehicle model database. Subsequently, the above steps can be repeated to periodically retrieve data from third-party vehicle model databases to update the online vehicle model database data in a timely manner, achieving dynamic and automatic updates.

[0068] Example 2:

[0069] This embodiment provides an automatic fusion and update system for multi-source vehicle model databases, such as... Figure 2 As shown, it includes a data retrieval unit, a structure verification unit, a mapping matching unit, a layer-by-layer review unit, and a data update unit, wherein:

[0070] The data retrieval unit is used to retrieve all third-party vehicle model database data to a third-party data cache table and to retrieve all online vehicle model database data to an online data cache table. The third-party vehicle model database data includes the third-party vehicle model data structure chain of the third-party vehicle model database, and the online vehicle model database data includes the online vehicle model data structure chain of the online vehicle model database.

[0071] The structure verification unit is used to perform structure verification on the data structure chains of each online vehicle model in the online data cache table and the data structure chains of each third-party vehicle model in the third-party data cache table to determine whether there are any structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table.

[0072] The mapping and matching unit is used to perform relationship mapping and similarity determination on the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table when it is determined that there is no structural change in the data structure chains of each third-party vehicle model in the third-party data cache table.

[0073] The layer-by-layer review unit is used to review and update the corresponding online vehicle data structure chain in the online data cache table layer by layer based on the relationship mapping and similarity judgment results, so as to obtain the reviewed and updated online data cache table.

[0074] The data update unit is used to update the data of each online vehicle model in the online vehicle model database by using the data structure chain of each online vehicle model in the online data cache table after the review and update.

[0075] Example 3:

[0076] This embodiment provides an automatic fusion and update system for multi-source vehicle model databases, such as... Figure 3 As shown, at the hardware level, it includes:

[0077] The data interface is used to establish data communication between the processor and external data terminals;

[0078] Memory, used to store instructions;

[0079] The processor is used to read the instructions stored in the memory and execute the multi-source vehicle model library automatic fusion and update method in Embodiment 1 according to the instructions.

[0080] Optionally, the system also includes an internal bus, through which the processor, memory, and data interface can be interconnected. This internal bus can be a PCIe (Peripheral Component Interconnect Eexpress) bus, which can be divided into an address bus, a data bus, a control bus, etc. The memory can include, but is not limited to, Random Access Memory (RAM), Read Only Memory (ROM), Flash Memory, First Input First Output (FIFO), and / or First In Last Out (FILO). The processor can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components.

[0081] Example 4:

[0082] This embodiment provides a computer-readable storage medium storing instructions. When these instructions are executed on a computer, the computer performs the automatic fusion and update method for the multi-source vehicle model database in Embodiment 1. The computer-readable storage medium refers to a data storage medium, which may include, but is not limited to, floppy disks, optical disks, hard disks, flash memory, USB flash drives, and / or Memory Sticks. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0083] This embodiment also provides a computer program product that, when run on a computer, executes the multi-source vehicle model database automatic fusion and update method described in Embodiment 1. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device.

[0084] Finally, it should be noted that the above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for automatic fusion and updating of multi-source vehicle model databases, characterized in that, include: Full data is retrieved from the third-party vehicle model database to the third-party data cache table, and full data is retrieved from the online vehicle model database to the online data cache table. The third-party vehicle model database data includes the third-party vehicle model data structure chain of the third-party vehicle model database, and the online vehicle model database data includes the online vehicle model data structure chain of the online vehicle model database. The method involves using the online vehicle model data structure chains in the online data cache table to perform structured verification on the third-party vehicle model data structure chains in the third-party data cache table to determine whether there are any structural changes in the third-party vehicle model data structure chains in the third-party data cache table. This verification includes: performing a structure mapping between the third-party vehicle model data structure chains in the third-party data cache table and the online vehicle model data structure chains in the online data cache table. If there is a situation where the lower-level data of a third-party vehicle model data structure chain is the same as the corresponding online vehicle model data structure chain, but the higher-level data is different, it is determined that there is a structural change in the corresponding third-party vehicle model data structure chain in the third-party data cache table; otherwise, it is determined that there is no structural change in the third-party vehicle model data structure chains in the third-party data cache table. When it is determined that there are no structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table, a relationship mapping and similarity determination are performed on the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table. This relationship mapping and similarity determination includes: cleaning the names of the data at each level of the data structure chains of each third-party vehicle model in the third-party data cache table to determine the ID primary key of each level of the data structure chains; performing a relationship mapping on the data structure chains of each online vehicle model in the online data cache table and the cleaned data structure chains of each third-party vehicle model in the third-party data cache table to determine the third-party vehicle model data structure chains and online vehicle model data structure chains with mapping relationships; and using the longest common subsequence method to determine the third-party vehicle model data structure chains without mapping relationships. The similarity between the third-party vehicle data structure chain and the online vehicle data structure chain is calculated pairwise to determine the similarity between the corresponding third-party vehicle data structure chain and the data at each level of the online vehicle data structure chain. Based on the relationship mapping and similarity judgment results, the corresponding online vehicle data structure chain in the online data cache table is reviewed and updated level by level, including: for third-party vehicle data structure chains and online vehicle data structure chains with mapping relationships, the data at each level of the online vehicle data structure chain is renamed directly using the ID primary key of the data at each level of the third-party vehicle data structure chain; for third-party vehicle data structure chains and online vehicle data structure chains without mapping relationships, a level-by-level review and comparison is performed. If the similarity between the two in the first M levels of data reaches the set similarity condition, the data at the first M levels of the online vehicle data structure chain is renamed accordingly using the ID primary key of the first M levels of data in the third-party vehicle data structure chain, where M is a positive integer in the interval [1, 3]. Based on the relationship mapping and similarity judgment results, the corresponding online vehicle data structure chain in the online data cache table is reviewed and updated layer by layer to obtain the reviewed and updated online data cache table; The online vehicle data in the online vehicle database is updated using the data structure chain of each online vehicle model in the updated online data cache table.

2. The method for automatic fusion and updating of multi-source vehicle model databases according to claim 1, characterized in that, Both the third-party vehicle data structure chain and the online vehicle data structure chain contain three levels of data: brand, manufacturer, and vehicle model. The structure levels are arranged from high to low as brand-manufacturer-vehicle model.

3. The method for automatic fusion and updating of multi-source vehicle model databases according to claim 2, characterized in that, The method further includes: when it is determined that there are structural changes in the data structure chain of each third-party vehicle model in the third-party data cache table, ending the update and issuing a warning message.

4. The method for automatic fusion and updating of multi-source vehicle model databases according to claim 1, characterized in that, The method further includes: When a third-party vehicle data structure chain does not have an online vehicle data structure chain with which it has a mapping relationship, and the similarity between the third-party vehicle data structure chain and the data at each level of the online vehicle data structure chain does not meet the set similarity conditions, the third-party vehicle data structure chain is directly added to the online data cache table as a new online vehicle data structure chain.

5. An automatic fusion and update system for multi-source vehicle model databases, characterized in that, It includes a data retrieval unit, a structure verification unit, a mapping matching unit, a layer-by-layer review unit, and a data update unit, among which: The data retrieval unit is used to retrieve all third-party vehicle model database data to a third-party data cache table and to retrieve all online vehicle model database data to an online data cache table. The third-party vehicle model database data includes the third-party vehicle model data structure chain of the third-party vehicle model database, and the online vehicle model database data includes the online vehicle model data structure chain of the online vehicle model database. The structure verification unit is used to perform structure verification on the data structure chains of each online vehicle model in the online data cache table and the data structure chains of each third-party vehicle model in the third-party data cache table to determine whether there are any structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table. This structure verification includes: performing a structure mapping between the data structure chains of each third-party vehicle model in the third-party data cache table and the data structure chains of each online vehicle model in the online data cache table; if there is a situation where the lower-level data of a third-party vehicle model data structure chain is the same as the corresponding online vehicle model data structure chain but the higher-level data is different, it is determined that the corresponding third-party vehicle model data structure chain in the third-party data cache table has a structural change; otherwise, it is determined that the data structure chains of each third-party vehicle model in the third-party data cache table do not have a structural change. The mapping and matching unit is used to perform relationship mapping and similarity determination on the data structure chains of third-party vehicle models in the third-party data cache table and the data structure chains of online vehicle models in the online data cache table when it is determined that there are no structural changes in the data structure chains of each third-party vehicle model in the third-party data cache table. The relationship mapping and similarity determination on the data structure chains of each third-party vehicle model in the third-party data cache table includes: cleaning the names of the data at each level of each third-party vehicle model data structure chain in the third-party data cache table to determine the ID primary key of each level of data in each third-party vehicle model data structure chain; performing relationship mapping on the data structure chains of each online vehicle model in the online data cache table and the cleaned data structure chains of each third-party vehicle model in the third-party data cache table to determine the third-party vehicle model data structure chains and online vehicle model data structure chains with mapping relationships; and using the longest common subsequence method to identify those without mapping relationships. The similarity of the third-party vehicle data structure chain and the online vehicle data structure chain is calculated pairwise to determine the similarity of each level of data between the corresponding third-party vehicle data structure chain and the online vehicle data structure chain. Based on the relationship mapping and similarity judgment results, the corresponding online vehicle data structure chain in the online data cache table is reviewed and updated level by level, including: for third-party vehicle data structure chains and online vehicle data structure chains with mapping relationships, the ID primary key of each level of data in the third-party vehicle data structure chain is used to rename each level of data in the online vehicle data structure chain; for third-party vehicle data structure chains and online vehicle data structure chains without mapping relationships, a level-by-level review and comparison is performed. If the similarity of the two in the first M levels of data reaches the set similarity condition, the ID primary key of the first M levels of data in the third-party vehicle data structure chain is used to rename the first M levels of data in the online vehicle data structure chain, where M is a positive integer in the interval [1, 3]. The layer-by-layer review unit is used to review and update the corresponding online vehicle data structure chain in the online data cache table layer by layer based on the relationship mapping and similarity judgment results, so as to obtain the reviewed and updated online data cache table. The data update unit is used to update the data of each online vehicle model in the online vehicle model database by using the data structure chain of each online vehicle model in the online data cache table after the review and update.

6. A multi-source vehicle model database automatic fusion and update system, characterized in that, include: Memory, used to store instructions; A processor is configured to read instructions stored in the memory and execute the automatic fusion and update method for multi-source vehicle model databases as described in any one of claims 1-4 according to the instructions.

7. A computer program product, characterized in that, When the computer program product is run on a computer, the automatic fusion and update method of the multi-source vehicle model database as described in any one of claims 1-4 is executed.

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